Permanent video monitoring for continuous improvement

Analyzing Machine Operation with Video

Several recent posts have focused on using video to analyze the performance of human operators in a production environment. However video can also be used to better understand how machines operate.

The following video clip shows how dough is extruded and then cut off by an automated knife to from the dough balls that will later become the waffles. If you watch the video clip, it seems that everything is automated and extremely regular and mechanical. There doesn’t appear to be any variation in the process.

To test this impression, I took the full video and constructed a “blend” that merged a segment from the video with another segment from the same video. If the process is really completely mechanical, the segments (once they are synchronized around the same starting point in the cycle) should pretty much overlap one another. You can see that “blended” video in the next clip.

As you watch the video, hover your mouse over the playbar and a panel of screenshots should pop up. Click on any of the first three thumbnails and watch the action. You should see that the dough balls from different times do not drop at exactly the same time, even though machine operation cycle has been matched quite closely. Then view either of the last two thumbnails. Here, you should see that the dough balls fall almost exactly at the same time. Again, the machine cycle is still closely synchronized.

What is happening? There appears to be considerable timing variation between the drop times for the dough balls, even though the machine operation is strictly mechanical.

The answer is that the overall machine operation is mechanical and regular. However, the actual extrusion and cutting of the dough balls is controlled by a sensor that measures whether the dough has completely filled the orifice. The dough material is not completely consistent. It has flour, a lot of margarine or butter and a lot of sugar and it has been allowed to “rise” for some time. Absolute material uniformity is impossible, and in an “artisan” baked product probably undesirable. That means that the extruder has to adjust itself to material non-uniformity at the same time that it has to drop the balls on a chain-driven conveyor.

None of this is obvious to the naked eye. However it is completely visible to the camera … if you can make effective comparisons.

When we originally recorded the video, we didn’t see the behavior until we constructed the blended video. At that point we asked the manager about the discrepancy and that is when we were told about action of the material sensor. By that time, we had already guessed at its existence solely from the odd synchronization we were seeing in the video.

Can you help?

I’m not an employee, investor or reseller for Dartfish, but they are kindly letting me play with it to explore any non-sports uses I can cook up. I am willing to play with videos sent to me by others as long as they don’t hold me to a deliverable timeline and they give me permission to post useful pieces on the blog.

Video for Operations Blog

DeeperPoint has been exploring applications of video (including the Dartfish video software technology) for analyzing, visualizing and coaching business operations ... especially in manufacturing, retail and logistics.
The following posts touch on different insights and observations from our explorations.